{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Softmax exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "This exercise is analogous to the SVM exercise. You will:\n",
    "\n",
    "- implement a fully-vectorized **loss function** for the Softmax classifier\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** with numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train data shape:  (49000, 3073)\nTrain labels shape:  (49000,)\nValidation data shape:  (1000, 3073)\nValidation labels shape:  (1000,)\nTest data shape:  (1000, 3073)\nTest labels shape:  (1000,)\ndev data shape:  (500, 3073)\ndev labels shape:  (500,)\n"
     ]
    }
   ],
   "source": [
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the linear classifier. These are the same steps as we used for the\n",
    "    SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    \n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "    X_dev = X_train[mask]\n",
    "    y_dev = y_train[mask]\n",
    "    \n",
    "    # Preprocessing: reshape the image data into rows\n",
    "    X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "    X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "    X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "    X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "    \n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis = 0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "    X_dev -= mean_image\n",
    "    \n",
    "    # add bias dimension and transform into columns\n",
    "    X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "    X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "    X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "    X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)\n",
    "print('dev labels shape: ', y_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Softmax Classifier\n",
    "\n",
    "Your code for this section will all be written inside `cs231n/classifiers/softmax.py`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "loss: 2.351947\nsanity check: 2.302585\n"
     ]
    }
   ],
   "source": [
    "# First implement the naive softmax loss function with nested loops.\n",
    "# Open the file cs231n/classifiers/softmax.py and implement the\n",
    "# softmax_loss_naive function.\n",
    "\n",
    "from cs231n.classifiers.softmax import softmax_loss_naive\n",
    "import time\n",
    "\n",
    "# Generate a random softmax weight matrix and use it to compute the loss.\n",
    "W = np.random.randn(3073, 10) * 0.0001\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As a rough sanity check, our loss should be something close to -log(0.1).\n",
    "print('loss: %f' % loss)\n",
    "print('sanity check: %f' % (-np.log(0.1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "Why do we expect our loss to be close to -log(0.1)? Explain briefly.**\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *Fill this in* \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "numerical: 2.660105 analytic: 2.660105, relative error: 2.637886e-08\n",
      "numerical: 1.721939 analytic: 1.721939, relative error: 4.751248e-08\n",
      "numerical: -1.315345 analytic: -1.315345, relative error: 1.779526e-08\n",
      "numerical: -0.114254 analytic: -0.114254, relative error: 5.336256e-07\n",
      "numerical: 1.569535 analytic: 1.569535, relative error: 3.644712e-08\n",
      "numerical: -1.727826 analytic: -1.727826, relative error: 6.179063e-09\n",
      "numerical: 2.333979 analytic: 2.333979, relative error: 1.646102e-08\n",
      "numerical: 2.520027 analytic: 2.520027, relative error: 2.107734e-08\n",
      "numerical: -1.492892 analytic: -1.492892, relative error: 1.169131e-08\n",
      "numerical: 0.921400 analytic: 0.921399, relative error: 6.540134e-08\n",
      "numerical: -2.647864 analytic: -2.647864, relative error: 3.343377e-08\n",
      "numerical: -1.175737 analytic: -1.175737, relative error: 1.749127e-08\n",
      "numerical: -0.862317 analytic: -0.862317, relative error: 4.524822e-09\n",
      "numerical: -3.839299 analytic: -3.839299, relative error: 3.959796e-09\n",
      "numerical: 0.239287 analytic: 0.239287, relative error: 4.775734e-08\n",
      "numerical: -0.923732 analytic: -0.923732, relative error: 2.512044e-09\n",
      "numerical: -1.616611 analytic: -1.616611, relative error: 3.484083e-08\n",
      "numerical: 1.689048 analytic: 1.689047, relative error: 4.204290e-08\n",
      "numerical: 0.632793 analytic: 0.632793, relative error: 1.530007e-07\n",
      "numerical: -4.489325 analytic: -4.489325, relative error: 1.523415e-08\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of softmax_loss_naive and implement a (naive)\n",
    "# version of the gradient that uses nested loops.\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As we did for the SVM, use numeric gradient checking as a debugging tool.\n",
    "# The numeric gradient should be close to the analytic gradient.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)\n",
    "\n",
    "# similar to SVM case, do another gradient check with regularization\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "naive loss: 2.351947e+00 computed in 0.293830s\nvectorized loss: 2.351947e+00 computed in 0.008997s\nLoss difference: 0.000000\nGradient difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Now that we have a naive implementation of the softmax loss function and its gradient,\n",
    "# implement a vectorized version in softmax_loss_vectorized.\n",
    "# The two versions should compute the same results, but the vectorized version should be\n",
    "# much faster.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.softmax import softmax_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# As we did for the SVM, we use the Frobenius norm to compare the two versions\n",
    "# of the gradient.\n",
    "grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('Loss difference: %f' % np.abs(loss_naive - loss_vectorized))\n",
    "print('Gradient difference: %f' % grad_difference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "tuning",
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.346980 val accuracy: 0.351000\nlr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.331082 val accuracy: 0.335000\nlr 5.000000e-07 reg 2.500000e+04 train accuracy: 0.342102 val accuracy: 0.358000\nlr 5.000000e-07 reg 5.000000e+04 train accuracy: 0.316571 val accuracy: 0.338000\nbest validation accuracy achieved during cross-validation: 0.358000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of over 0.35 on the validation set.\n",
    "\n",
    "from cs231n.classifiers import Softmax\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_softmax = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained softmax classifer in best_softmax.                          #\n",
    "################################################################################\n",
    "\n",
    "# Provided as a reference. You may or may not want to change these hyperparameters\n",
    "learning_rates = [1e-7, 5e-7]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "pass\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        softmax = Softmax()\n",
    "        loss_hist=softmax.train(X_train,y_train,lr,rs,num_iters=1500)\n",
    "        y_train_pred=softmax.predict(X_train)\n",
    "        tra_accuracy=np.mean(y_train==y_train_pred)\n",
    "        y_val_pred=softmax.predict(X_val)\n",
    "        val_accuracy=np.mean(y_val==y_val_pred)\n",
    "\n",
    "        results[(lr,rs)]=(tra_accuracy,val_accuracy)\n",
    "        if(val_accuracy>best_val):\n",
    "            best_val=val_accuracy\n",
    "            best_softmax=softmax\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "test"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "softmax on raw pixels final test set accuracy: 0.351000\n"
     ]
    }
   ],
   "source": [
    "# evaluate on test set\n",
    "# Evaluate the best softmax on test set\n",
    "y_test_pred = best_softmax.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('softmax on raw pixels final test set accuracy: %f' % (test_accuracy, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 2** - *True or False*\n",
    "\n",
    "Suppose the overall training loss is defined as the sum of the per-datapoint loss over all training examples. It is possible to add a new datapoint to a training set that would leave the SVM loss unchanged, but this is not the case with the Softmax classifier loss.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Visualize the learned weights for each class\n",
    "w = best_softmax.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "    \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}